Fix CUDA OOM by mutually unloading Whisper and ChatTTS on 8GB GPU.

Release GPU memory before TTS/ASR switches, lower TTS token limits, and set PYTORCH_CUDA_ALLOC_CONF in PM2.

Co-authored-by: Cursor <cursoragent@cursor.com>

.env.example

@@ -13,3 +13,7 @@ OLLAMA_PORT=11434
 # WHISPER_MODEL_DIR=/opt/Trading_Studio/models/whisper
 # WHISPER_MODEL_SIZE=small
 # HF_ENDPOINT=https://hf-mirror.com
+
+# 8GB 显存 OOM 时可调低（合成按段切分）
+# TTS_MAX_CHARS_PER_CHUNK=150
+# TTS_MAX_NEW_TOKEN=768

DEPLOY.md

@@ -733,10 +733,26 @@ nvidia-smi
 fuser -v /dev/nvidia*
 ```
 
-Whisper 与 ChatTTS 不会同时常驻最大显存，但首次加载模型时峰值较高。建议：
+Whisper 与 ChatTTS **不能同时常驻** 8GB 显存（会 CUDA OOM）。应用已自动互斥卸载：
 
-- 锁定 120W 功耗墙
+- 识别前卸载 ChatTTS
-- `max_memory_restart: "6G"` 已在 PM2 配置中设置
+- 合成 / 锁定音色前卸载 Whisper
+
+若仍 OOM：
+
+```bash
+pm2 restart trading_studio
+nvidia-smi   # 确认无其他占 GPU 进程
+```
+
+在 `.env` 调低合成峰值：
+
+```ini
+TTS_MAX_CHARS_PER_CHUNK=150
+TTS_MAX_NEW_TOKEN=768
+```
+
+PM2 已配置 `PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True` 缓解碎片。建议锁定 120W 功耗墙。
 
 ### 10.3 Whisper 模型加载失败
 

config.py

@@ -136,8 +136,11 @@ TTS_TOP_P = 0.7
 TTS_TOP_K = 20
 TTS_SPEED_PROMPT = "[speed_5]"
 
-# 单段 TTS 最大字数（超长稿按句切分后逐段合成再拼接）
+# 单段 TTS 最大字数（超长稿按句切分后逐段合成再拼接；8GB 显存建议 ≤200）
-TTS_MAX_CHARS_PER_CHUNK = _env_int("TTS_MAX_CHARS_PER_CHUNK", 280)
+TTS_MAX_CHARS_PER_CHUNK = _env_int("TTS_MAX_CHARS_PER_CHUNK", 200)
+
+# ChatTTS 单段最大生成 token（越小越省显存，长句会自动切多段）
+TTS_MAX_NEW_TOKEN = _env_int("TTS_MAX_NEW_TOKEN", 1024)
 
 # ---------------------------------------------------------------------------
 # 上传临时文件目录

ecosystem.config.js

@@ -21,6 +21,7 @@ module.exports = {
       env: {
         PYTHONUNBUFFERED: "1",
         CUDA_VISIBLE_DEVICES: "0",
+        PYTORCH_CUDA_ALLOC_CONF: "expandable_segments:True",
       },
       error_file: path.join(APP_DIR, "logs/pm2-error.log"),
       out_file: path.join(APP_DIR, "logs/pm2-out.log"),

gpu_utils.py

@@ -0,0 +1,35 @@
+"""GPU 显存回收工具（3060 Ti 8GB：Whisper 与 ChatTTS 不宜同时驻留）。"""
+
+from __future__ import annotations
+
+import gc
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+def release_cuda_cache() -> None:
+    """触发 GC 并清空 PyTorch CUDA 缓存。"""
+    gc.collect()
+    try:
+        import torch
+
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+            if hasattr(torch.cuda, "ipc_collect"):
+                torch.cuda.ipc_collect()
+    except ImportError:
+        pass
+
+
+def cuda_memory_summary() -> str:
+    """返回简要显存占用（调试用）。"""
+    try:
+        import torch
+
+        if not torch.cuda.is_available():
+            return "CUDA 不可用"
+        free, total = torch.cuda.mem_get_info()
+        return f"GPU 显存: 已用 {(total - free) / 1024**3:.2f}GB / {total / 1024**3:.2f}GB"
+    except Exception as exc:
+        return f"显存查询失败: {exc}"

tts_service.py

@@ -31,6 +31,7 @@ from config import (
     SPEAKER_SAMPLE_MAX_SEC,
     SPEAKER_SAMPLE_MIN_SEC,
     TTS_MAX_CHARS_PER_CHUNK,
+    TTS_MAX_NEW_TOKEN,
     TTS_SAMPLE_RATE,
     TTS_SPEED_PROMPT,
     TTS_TEMPERATURE,
@@ -92,7 +93,7 @@ def _load_chat_model(chat) -> None:
     _ensure_hf_env()
     model_dir = CHATTTS_MODEL_DIR
 
-    base_kwargs: Dict[str, Any] = {"compile": False}
+    base_kwargs: Dict[str, Any] = {"compile": False, "enable_cache": False}
 
     if not hasattr(chat, "load"):
         if hasattr(chat, "load_models"):
@@ -137,11 +138,26 @@ def _load_chat_model(chat) -> None:
 
 
 def reset_chattts_instance() -> None:
-    """释放 ChatTTS 实例（模型下载后重启前可调用）。"""
+    """卸载 ChatTTS 模型并回收 GPU 显存。"""
     global _chat, _chat_error
+    if _chat is not None:
+        try:
+            if hasattr(_chat, "unload"):
+                _chat.unload()
+        except Exception:
+            logger.exception("ChatTTS unload 失败")
+        try:
+            del _chat
+        except Exception:
+            pass
     _chat = None
     _chat_error = None
 
+    from gpu_utils import release_cuda_cache
+
+    release_cuda_cache()
+    logger.info("ChatTTS 模型已卸载，显存已尝试回收。")
+
 
 def get_chattts_instance():
     """
@@ -348,6 +364,13 @@ def save_fixed_speaker(
     if not audio_sample_path:
         return False, "未提供音色参考音频。"
 
+    try:
+        from whisper_service import reset_whisper_model
+
+        reset_whisper_model()
+    except Exception:
+        logger.debug("锁定音色前释放 Whisper 显存跳过", exc_info=True)
+
     chat, init_err = get_chattts_instance()
     if chat is None:
         return False, init_err or "ChatTTS 不可用。"
@@ -566,6 +589,19 @@ def generate_voice(refined_text: str) -> Tuple[bool, str, Optional[str]]:
     if not refined_text or not refined_text.strip():
         return False, "合成文本为空，请先完成润色。", None
 
+    # 合成前释放 Whisper，避免 8GB 显存上双模型 OOM
+    try:
+        from whisper_service import reset_whisper_model
+
+        reset_whisper_model()
+    except Exception:
+        logger.debug("合成前释放 Whisper 显存跳过", exc_info=True)
+
+    from gpu_utils import cuda_memory_summary, release_cuda_cache
+
+    release_cuda_cache()
+    logger.info("TTS 合成前 %s", cuda_memory_summary())
+
     chat, init_err = get_chattts_instance()
     if chat is None:
         return False, init_err or "ChatTTS 不可用。", None
@@ -604,6 +640,7 @@ def generate_voice(refined_text: str) -> Tuple[bool, str, Optional[str]]:
             temperature=TTS_TEMPERATURE,
             top_P=TTS_TOP_P,
             top_K=TTS_TOP_K,
+            max_new_token=TTS_MAX_NEW_TOKEN,
         )
 
         params_refine_text = ChatTTS.Chat.RefineTextParams(
@@ -619,9 +656,10 @@ def generate_voice(refined_text: str) -> Tuple[bool, str, Optional[str]]:
 
         segment_wavs: List[np.ndarray] = []
         for idx, chunk in enumerate(chunks, start=1):
+            release_cuda_cache()
             wavs = chat.infer(
                 chunk,
-                skip_refine_text=False,
+                skip_refine_text=(idx > 1),
                 params_refine_text=params_refine_text,
                 params_infer_code=params_infer_code,
             )
@@ -633,6 +671,7 @@ def generate_voice(refined_text: str) -> Tuple[bool, str, Optional[str]]:
                     None,
                 )
             segment_wavs.append(np.asarray(wavs[0], dtype=np.float32))
+            release_cuda_cache()
 
         wav_array = (
             segment_wavs[0]
@@ -661,7 +700,19 @@ def generate_voice(refined_text: str) -> Tuple[bool, str, Optional[str]]:
 
     except Exception as exc:
         exc_msg = str(exc)
-        if "Corrupt input data" in exc_msg:
+        if "out of memory" in exc_msg.lower() or "OutOfMemoryError" in exc_msg:
+            release_cuda_cache()
+            err = (
+                "语音合成失败: GPU 显存不足（CUDA OOM）。\n"
+                "3060 Ti 8GB 无法同时运行 Whisper + ChatTTS。\n"
+                "处理步骤：\n"
+                "1. pm2 restart trading_studio 释放显存\n"
+                "2. 不要连续快速点识别+合成；合成前系统会自动卸载 Whisper\n"
+                "3. 若仍 OOM，在 .env 设置 TTS_MAX_CHARS_PER_CHUNK=150、TTS_MAX_NEW_TOKEN=768\n"
+                "4. 确认无其他程序占用 GPU: nvidia-smi\n"
+                f"技术详情: {exc_msg[:400]}"
+            )
+        elif "Corrupt input data" in exc_msg:
             err = (
                 "语音合成失败: 音色数据损坏或格式不兼容（Corrupt input data）。\n"
                 "处理步骤：\n"

whisper_service.py

@@ -156,6 +156,14 @@ def transcribe_audio(audio_path: str) -> Tuple[bool, str]:
     if not audio_path:
         return False, "未提供音频文件路径。"
 
+    # 识别前释放 ChatTTS，避免与 Whisper 同占 8GB 显存
+    try:
+        from tts_service import reset_chattts_instance
+
+        reset_chattts_instance()
+    except Exception:
+        logger.debug("释放 ChatTTS 显存时跳过", exc_info=True)
+
     model, init_error = get_whisper_model()
     if model is None:
         return False, init_error or "Whisper 模型不可用。"
@@ -199,6 +207,17 @@ def transcribe_audio(audio_path: str) -> Tuple[bool, str]:
 
 
 def reset_whisper_model() -> None:
+    """卸载 Whisper 模型并回收 GPU 显存。"""
     global _model, _model_error
+    if _model is not None:
+        try:
+            del _model
+        except Exception:
+            pass
     _model = None
     _model_error = None
+
+    from gpu_utils import release_cuda_cache
+
+    release_cuda_cache()
+    logger.info("Whisper 模型已卸载，显存已尝试回收。")